标题： 基于可解释人工智能的供气温度预测系统 显示英文标题

标题： Explainable AI based System for Supply Air Temperature Forecast

摘要： 本文探讨了可解释人工智能（XAI）技术在改善自动化送风温度（ASAT）控制中预测模型的透明度和理解方面的应用。 该研究重点是使用带有Huber损失的线性回归进行ASAT预测。 然而，仅有一个控制曲线而没有语义和/或物理解释通常是不够的。 本研究采用了一种XAI方法：Shapley值，这有助于揭示推理并突出每个特征对最终ASAT预测的贡献。 与其他XAI方法相比，Shapley值具有坚实的数学基础，从而实现了解释的透明度。 该研究展示了对比解释——对于每个ASAT的控制值，可以给出客户关于曲线变化的客观理由。 显示英文摘要

摘要： This paper explores the application of Explainable AI (XAI) techniques to improve the transparency and understanding of predictive models in control of automated supply air temperature (ASAT) of Air Handling Unit (AHU). The study focuses on forecasting of ASAT using a linear regression with Huber loss. However, having only a control curve without semantic and/or physical explanation is often not enough. The present study employs one of the XAI methods: Shapley values, which allows to reveal the reasoning and highlight the contribution of each feature to the final ASAT forecast. In comparison to other XAI methods, Shapley values have solid mathematical background, resulting in interpretation transparency. The study demonstrates the contrastive explanations--slices, for each control value of ASAT, which makes it possible to give the client objective justifications for curve changes.